Overview

Brought to you by YData

Dataset statistics

Number of variables9
Number of observations6150
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.0 MiB
Average record size in memory173.2 B

Variable types

Text2
Numeric7

Alerts

% pop >= 25 ug/m3 [%] is highly overall correlated with Geographic-Mean PM2.5 [ug/m3] and 2 other fieldsHigh correlation
Geographic Coverage [%] is highly overall correlated with Population Coverage [%]High correlation
Geographic-Mean PM2.5 [ug/m3] is highly overall correlated with % pop >= 25 ug/m3 [%] and 2 other fieldsHigh correlation
Population Coverage [%] is highly overall correlated with Geographic Coverage [%]High correlation
Population-Weighted PM2.5 [ug/m3] is highly overall correlated with % pop >= 25 ug/m3 [%] and 2 other fieldsHigh correlation
Total Population [million people] is highly overall correlated with % pop >= 25 ug/m3 [%] and 2 other fieldsHigh correlation
Total Population [million people] has 100 (1.6%) zeros Zeros
% pop >= 25 ug/m3 [%] has 3025 (49.2%) zeros Zeros

Reproduction

Analysis started2025-04-05 12:58:51.138786
Analysis finished2025-04-05 12:58:56.014065
Duration4.88 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Region
Text

Distinct246
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size353.8 KiB
2025-04-05T13:58:56.244368image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length32
Median length22
Mean length9.3617886
Min length4

Characters and Unicode

Total characters57575
Distinct characters61
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAruba
2nd rowAruba
3rd rowAruba
4th rowAruba
5th rowAruba
ValueCountFrequency (%)
islands 325
 
3.8%
and 275
 
3.2%
republic 125
 
1.5%
saint 125
 
1.5%
united 75
 
0.9%
guinea 75
 
0.9%
south 75
 
0.9%
of 75
 
0.9%
new 75
 
0.9%
cyprus 50
 
0.6%
Other values (278) 7200
85.0%
2025-04-05T13:58:56.627963image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 8425
14.6%
n 4800
 
8.3%
i 4675
 
8.1%
e 3850
 
6.7%
r 3275
 
5.7%
o 2975
 
5.2%
2325
 
4.0%
s 2325
 
4.0%
l 2300
 
4.0%
t 2300
 
4.0%
Other values (51) 20325
35.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 46825
81.3%
Uppercase Letter 8200
 
14.2%
Space Separator 2325
 
4.0%
Other Punctuation 125
 
0.2%
Dash Punctuation 100
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 8425
18.0%
n 4800
10.3%
i 4675
10.0%
e 3850
 
8.2%
r 3275
 
7.0%
o 2975
 
6.4%
s 2325
 
5.0%
l 2300
 
4.9%
t 2300
 
4.9%
u 2175
 
4.6%
Other values (21) 9725
20.8%
Uppercase Letter
ValueCountFrequency (%)
S 1025
12.5%
M 725
 
8.8%
C 700
 
8.5%
I 625
 
7.6%
B 575
 
7.0%
G 500
 
6.1%
A 500
 
6.1%
N 425
 
5.2%
T 400
 
4.9%
P 400
 
4.9%
Other values (15) 2325
28.4%
Other Punctuation
ValueCountFrequency (%)
. 50
40.0%
; 50
40.0%
' 25
20.0%
Space Separator
ValueCountFrequency (%)
2325
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 55025
95.6%
Common 2550
 
4.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 8425
15.3%
n 4800
 
8.7%
i 4675
 
8.5%
e 3850
 
7.0%
r 3275
 
6.0%
o 2975
 
5.4%
s 2325
 
4.2%
l 2300
 
4.2%
t 2300
 
4.2%
u 2175
 
4.0%
Other values (46) 17925
32.6%
Common
ValueCountFrequency (%)
2325
91.2%
- 100
 
3.9%
. 50
 
2.0%
; 50
 
2.0%
' 25
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 57425
99.7%
None 150
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 8425
14.7%
n 4800
 
8.4%
i 4675
 
8.1%
e 3850
 
6.7%
r 3275
 
5.7%
o 2975
 
5.2%
2325
 
4.0%
s 2325
 
4.0%
l 2300
 
4.0%
t 2300
 
4.0%
Other values (46) 20175
35.1%
None
ValueCountFrequency (%)
é 50
33.3%
ã 25
16.7%
í 25
16.7%
ô 25
16.7%
ç 25
16.7%

Year
Real number (ℝ)

Distinct25
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2010
Minimum1998
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size48.2 KiB
2025-04-05T13:58:56.742458image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1998
5-th percentile1999
Q12004
median2010
Q32016
95-th percentile2021
Maximum2022
Range24
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.2116889
Coefficient of variation (CV)0.0035879049
Kurtosis-1.2038492
Mean2010
Median Absolute Deviation (MAD)6
Skewness0
Sum12361500
Variance52.008457
MonotonicityNot monotonic
2025-04-05T13:58:56.833030image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
1998 246
 
4.0%
2011 246
 
4.0%
2021 246
 
4.0%
2020 246
 
4.0%
2019 246
 
4.0%
2018 246
 
4.0%
2017 246
 
4.0%
2016 246
 
4.0%
2015 246
 
4.0%
2014 246
 
4.0%
Other values (15) 3690
60.0%
ValueCountFrequency (%)
1998 246
4.0%
1999 246
4.0%
2000 246
4.0%
2001 246
4.0%
2002 246
4.0%
2003 246
4.0%
2004 246
4.0%
2005 246
4.0%
2006 246
4.0%
2007 246
4.0%
ValueCountFrequency (%)
2022 246
4.0%
2021 246
4.0%
2020 246
4.0%
2019 246
4.0%
2018 246
4.0%
2017 246
4.0%
2016 246
4.0%
2015 246
4.0%
2014 246
4.0%
2013 246
4.0%

Population-Weighted PM2.5 [ug/m3]
Real number (ℝ)

High correlation 

Distinct648
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.258439
Minimum1.4
Maximum106
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size48.2 KiB
2025-04-05T13:58:56.954980image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1.4
5-th percentile4.5
Q110.2
median17.3
Q326.5
95-th percentile48.155
Maximum106
Range104.6
Interquartile range (IQR)16.3

Descriptive statistics

Standard deviation13.99098
Coefficient of variation (CV)0.69062477
Kurtosis3.5704522
Mean20.258439
Median Absolute Deviation (MAD)7.8
Skewness1.5581834
Sum124589.4
Variance195.74751
MonotonicityNot monotonic
2025-04-05T13:58:57.146789image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.7 42
 
0.7%
4.4 39
 
0.6%
4.3 39
 
0.6%
4.5 38
 
0.6%
4.6 36
 
0.6%
10.4 34
 
0.6%
18.2 33
 
0.5%
10.7 32
 
0.5%
12.1 32
 
0.5%
9.2 32
 
0.5%
Other values (638) 5793
94.2%
ValueCountFrequency (%)
1.4 1
 
< 0.1%
1.5 1
 
< 0.1%
1.6 6
0.1%
1.7 2
 
< 0.1%
1.8 5
0.1%
1.9 5
0.1%
2 4
0.1%
2.1 1
 
< 0.1%
2.5 2
 
< 0.1%
2.6 1
 
< 0.1%
ValueCountFrequency (%)
106 1
< 0.1%
102.1 1
< 0.1%
101.2 1
< 0.1%
100.9 1
< 0.1%
99.3 1
< 0.1%
98.1 1
< 0.1%
97.3 1
< 0.1%
96.5 1
< 0.1%
94.8 1
< 0.1%
94.7 1
< 0.1%

Geographic-Mean PM2.5 [ug/m3]
Real number (ℝ)

High correlation 

Distinct633
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.889772
Minimum1.1
Maximum105.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size48.2 KiB
2025-04-05T13:58:57.383362image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1.1
5-th percentile4.4
Q19.8
median16.9
Q325.3
95-th percentile48
Maximum105.7
Range104.6
Interquartile range (IQR)15.5

Descriptive statistics

Standard deviation13.963481
Coefficient of variation (CV)0.70204329
Kurtosis3.0352467
Mean19.889772
Median Absolute Deviation (MAD)7.5
Skewness1.5062927
Sum122322.1
Variance194.97881
MonotonicityNot monotonic
2025-04-05T13:58:57.516921image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.4 46
 
0.7%
4.3 43
 
0.7%
4.7 43
 
0.7%
4.8 41
 
0.7%
4.6 40
 
0.7%
4.5 36
 
0.6%
4.9 36
 
0.6%
10.1 36
 
0.6%
16.9 35
 
0.6%
9.2 35
 
0.6%
Other values (623) 5759
93.6%
ValueCountFrequency (%)
1.1 2
 
< 0.1%
1.2 6
0.1%
1.3 8
0.1%
1.4 6
0.1%
1.5 1
 
< 0.1%
1.7 2
 
< 0.1%
2.5 2
 
< 0.1%
2.8 6
0.1%
2.9 3
 
< 0.1%
3 4
0.1%
ValueCountFrequency (%)
105.7 1
< 0.1%
102.2 1
< 0.1%
97.2 1
< 0.1%
96 1
< 0.1%
94.5 1
< 0.1%
93.9 1
< 0.1%
93.5 1
< 0.1%
93.3 1
< 0.1%
90.3 1
< 0.1%
89.2 1
< 0.1%

Population Coverage [%]
Real number (ℝ)

High correlation 

Distinct54
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.582943
Minimum68.2
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size48.2 KiB
2025-04-05T13:58:57.825612image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum68.2
5-th percentile99.5
Q199.9
median100
Q3100
95-th percentile100
Maximum100
Range31.8
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation2.7229024
Coefficient of variation (CV)0.02734306
Kurtosis89.651057
Mean99.582943
Median Absolute Deviation (MAD)0
Skewness-9.1609303
Sum612435.1
Variance7.4141975
MonotonicityNot monotonic
2025-04-05T13:58:57.972639image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 3979
64.7%
99.9 1149
 
18.7%
99.8 341
 
5.5%
99.7 191
 
3.1%
99.6 115
 
1.9%
99.1 73
 
1.2%
99.5 72
 
1.2%
98.7 35
 
0.6%
68.2 25
 
0.4%
99.2 24
 
0.4%
Other values (44) 146
 
2.4%
ValueCountFrequency (%)
68.2 25
0.4%
74.1 3
 
< 0.1%
74.8 1
 
< 0.1%
75.4 1
 
< 0.1%
76 1
 
< 0.1%
76.6 1
 
< 0.1%
77.1 1
 
< 0.1%
77.8 1
 
< 0.1%
78.4 1
 
< 0.1%
78.9 1
 
< 0.1%
ValueCountFrequency (%)
100 3979
64.7%
99.9 1149
 
18.7%
99.8 341
 
5.5%
99.7 191
 
3.1%
99.6 115
 
1.9%
99.5 72
 
1.2%
99.4 3
 
< 0.1%
99.3 3
 
< 0.1%
99.2 24
 
0.4%
99.1 73
 
1.2%

Geographic Coverage [%]
Real number (ℝ)

High correlation 

Distinct48
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98.2
Minimum1.7
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size48.2 KiB
2025-04-05T13:58:58.107550image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1.7
5-th percentile95
Q199.2
median99.8
Q3100
95-th percentile100
Maximum100
Range98.3
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation8.2522438
Coefficient of variation (CV)0.08403507
Kurtosis88.705043
Mean98.2
Median Absolute Deviation (MAD)0.2
Skewness-8.9298479
Sum603930
Variance68.099528
MonotonicityNot monotonic
2025-04-05T13:58:58.239092image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
100 2075
33.7%
99.9 850
13.8%
99.8 550
 
8.9%
99.7 275
 
4.5%
99.4 275
 
4.5%
99.6 225
 
3.7%
99.3 175
 
2.8%
99.2 150
 
2.4%
99.5 150
 
2.4%
99 75
 
1.2%
Other values (38) 1350
22.0%
ValueCountFrequency (%)
1.7 25
0.4%
42.6 25
0.4%
45 25
0.4%
80 25
0.4%
81.3 25
0.4%
86.7 25
0.4%
90.5 25
0.4%
90.9 25
0.4%
93.3 25
0.4%
93.7 25
0.4%
ValueCountFrequency (%)
100 2075
33.7%
99.9 850
13.8%
99.8 550
 
8.9%
99.7 275
 
4.5%
99.6 225
 
3.7%
99.5 150
 
2.4%
99.4 275
 
4.5%
99.3 175
 
2.8%
99.2 150
 
2.4%
99.1 75
 
1.2%

Total Population [million people]
Real number (ℝ)

High correlation  Zeros 

Distinct3709
Distinct (%)60.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.031638
Minimum0
Maximum1398.52
Zeros100
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size48.2 KiB
2025-04-05T13:58:58.342607image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.005
Q10.2175
median4.186
Q316.99775
95-th percentile93.203
Maximum1398.52
Range1398.52
Interquartile range (IQR)16.78025

Descriptive statistics

Standard deviation119.91563
Coefficient of variation (CV)4.2778674
Kurtosis97.478083
Mean28.031638
Median Absolute Deviation (MAD)4.1505
Skewness9.5345053
Sum172394.57
Variance14379.759
MonotonicityNot monotonic
2025-04-05T13:58:58.461396image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.002 125
 
2.0%
0 100
 
1.6%
0.004 65
 
1.1%
0.012 49
 
0.8%
0.005 44
 
0.7%
0.011 41
 
0.7%
0.023 26
 
0.4%
0.044 26
 
0.4%
0.013 24
 
0.4%
0.099 24
 
0.4%
Other values (3699) 5626
91.5%
ValueCountFrequency (%)
0 100
1.6%
0.002 125
2.0%
0.003 2
 
< 0.1%
0.004 65
1.1%
0.005 44
 
0.7%
0.006 7
 
0.1%
0.007 7
 
0.1%
0.008 20
 
0.3%
0.009 10
 
0.2%
0.01 4
 
0.1%
ValueCountFrequency (%)
1398.52 3
< 0.1%
1393.215 1
 
< 0.1%
1387.91 1
 
< 0.1%
1386.215 3
< 0.1%
1382.604 1
 
< 0.1%
1377.299 1
 
< 0.1%
1371.993 1
 
< 0.1%
1370.661 1
 
< 0.1%
1365.032 1
 
< 0.1%
1358.071 1
 
< 0.1%

% pop >= 25 ug/m3 [%]
Real number (ℝ)

High correlation  Zeros 

Distinct860
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.029431
Minimum0
Maximum100
Zeros3025
Zeros (%)49.2%
Negative0
Negative (%)0.0%
Memory size48.2 KiB
2025-04-05T13:58:58.574488image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.1
Q354.2
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)54.2

Descriptive statistics

Standard deviation38.409951
Coefficient of variation (CV)1.4210418
Kurtosis-0.6739349
Mean27.029431
Median Absolute Deviation (MAD)0.1
Skewness1.0207629
Sum166231
Variance1475.3243
MonotonicityNot monotonic
2025-04-05T13:58:58.680269image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3025
49.2%
100 500
 
8.1%
0.1 64
 
1.0%
99.9 52
 
0.8%
0.2 41
 
0.7%
0.3 32
 
0.5%
0.4 25
 
0.4%
99.7 25
 
0.4%
99.8 20
 
0.3%
0.5 20
 
0.3%
Other values (850) 2346
38.1%
ValueCountFrequency (%)
0 3025
49.2%
0.1 64
 
1.0%
0.2 41
 
0.7%
0.3 32
 
0.5%
0.4 25
 
0.4%
0.5 20
 
0.3%
0.6 18
 
0.3%
0.7 19
 
0.3%
0.8 11
 
0.2%
0.9 14
 
0.2%
ValueCountFrequency (%)
100 500
8.1%
99.9 52
 
0.8%
99.8 20
 
0.3%
99.7 25
 
0.4%
99.6 16
 
0.3%
99.5 17
 
0.3%
99.4 13
 
0.2%
99.3 8
 
0.1%
99.2 12
 
0.2%
99.1 13
 
0.2%

Region_normalized
Text

Distinct246
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size350.4 KiB
2025-04-05T13:58:58.984616image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length32
Median length24
Mean length9.3252033
Min length4

Characters and Unicode

Total characters57350
Distinct characters27
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowaruba
2nd rowaruba
3rd rowaruba
4th rowaruba
5th rowaruba
ValueCountFrequency (%)
islands 325
 
3.8%
and 275
 
3.2%
republic 125
 
1.5%
saint 125
 
1.5%
united 75
 
0.9%
guinea 75
 
0.9%
south 75
 
0.9%
of 75
 
0.9%
new 75
 
0.9%
cyprus 50
 
0.6%
Other values (278) 7200
85.0%
2025-04-05T13:58:59.482177image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 8950
15.6%
i 5325
 
9.3%
n 5225
 
9.1%
e 4125
 
7.2%
r 3550
 
6.2%
s 3350
 
5.8%
o 3025
 
5.3%
t 2700
 
4.7%
l 2625
 
4.6%
u 2375
 
4.1%
Other values (17) 16100
28.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 55025
95.9%
Space Separator 2325
 
4.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 8950
16.3%
i 5325
 
9.7%
n 5225
 
9.5%
e 4125
 
7.5%
r 3550
 
6.5%
s 3350
 
6.1%
o 3025
 
5.5%
t 2700
 
4.9%
l 2625
 
4.8%
u 2375
 
4.3%
Other values (16) 13775
25.0%
Space Separator
ValueCountFrequency (%)
2325
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 55025
95.9%
Common 2325
 
4.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 8950
16.3%
i 5325
 
9.7%
n 5225
 
9.5%
e 4125
 
7.5%
r 3550
 
6.5%
s 3350
 
6.1%
o 3025
 
5.5%
t 2700
 
4.9%
l 2625
 
4.8%
u 2375
 
4.3%
Other values (16) 13775
25.0%
Common
ValueCountFrequency (%)
2325
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 57350
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 8950
15.6%
i 5325
 
9.3%
n 5225
 
9.1%
e 4125
 
7.2%
r 3550
 
6.2%
s 3350
 
5.8%
o 3025
 
5.3%
t 2700
 
4.7%
l 2625
 
4.6%
u 2375
 
4.1%
Other values (17) 16100
28.1%

Interactions

2025-04-05T13:58:55.129841image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:51.393316image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:52.138946image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:52.770428image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:53.388745image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:53.947620image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:54.537120image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:55.213360image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:51.494990image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:52.210489image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:52.863051image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:53.460454image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:54.018136image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:54.613667image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:55.292092image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:51.585518image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:52.287807image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:52.948074image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:53.538821image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:54.100327image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:54.696347image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:55.413105image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:51.663032image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:52.362930image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:53.022440image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:53.614295image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:54.191429image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:54.771050image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:55.516437image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:51.756045image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:52.440446image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:53.116005image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:53.694367image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:54.284966image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:54.856136image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:55.607993image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:51.897085image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:52.520975image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:53.200692image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:53.782971image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:54.371654image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:54.940692image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:55.702572image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:52.059531image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:52.652497image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:53.294212image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:53.866579image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:54.452782image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:55.036254image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2025-04-05T13:58:59.572784image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
% pop >= 25 ug/m3 [%]Geographic Coverage [%]Geographic-Mean PM2.5 [ug/m3]Population Coverage [%]Population-Weighted PM2.5 [ug/m3]Total Population [million people]Year
% pop >= 25 ug/m3 [%]1.000-0.0160.8980.0470.9130.527-0.012
Geographic Coverage [%]-0.0161.0000.1040.5670.080-0.0680.000
Geographic-Mean PM2.5 [ug/m3]0.8980.1041.0000.1430.9780.547-0.027
Population Coverage [%]0.0470.5670.1431.0000.137-0.0010.007
Population-Weighted PM2.5 [ug/m3]0.9130.0800.9780.1371.0000.582-0.022
Total Population [million people]0.527-0.0680.547-0.0010.5821.0000.037
Year-0.0120.000-0.0270.007-0.0220.0371.000

Missing values

2025-04-05T13:58:55.818087image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-05T13:58:55.950401image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

RegionYearPopulation-Weighted PM2.5 [ug/m3]Geographic-Mean PM2.5 [ug/m3]Population Coverage [%]Geographic Coverage [%]Total Population [million people]% pop >= 25 ug/m3 [%]Region_normalized
0Aruba19989.59.3100.0100.00.0880.0aruba
1Aruba199910.19.9100.0100.00.0880.0aruba
2Aruba200010.210.0100.0100.00.0880.0aruba
3Aruba200110.610.4100.0100.00.0900.0aruba
4Aruba200210.510.3100.0100.00.0920.0aruba
5Aruba200311.411.2100.0100.00.0930.0aruba
6Aruba200410.19.9100.0100.00.0950.0aruba
7Aruba200510.410.2100.0100.00.0970.0aruba
8Aruba200610.410.2100.0100.00.0970.0aruba
9Aruba200710.710.5100.0100.00.0980.0aruba
RegionYearPopulation-Weighted PM2.5 [ug/m3]Geographic-Mean PM2.5 [ug/m3]Population Coverage [%]Geographic Coverage [%]Total Population [million people]% pop >= 25 ug/m3 [%]Region_normalized
6140Zimbabwe201317.316.7100.099.414.9400.0zimbabwe
6141Zimbabwe201415.314.4100.099.415.2650.0zimbabwe
6142Zimbabwe201517.917.2100.099.415.5910.0zimbabwe
6143Zimbabwe201615.815.0100.099.415.9640.0zimbabwe
6144Zimbabwe201715.715.0100.099.416.3380.0zimbabwe
6145Zimbabwe201818.117.3100.099.416.7110.0zimbabwe
6146Zimbabwe201916.615.6100.099.417.0850.0zimbabwe
6147Zimbabwe202013.813.3100.099.417.4580.0zimbabwe
6148Zimbabwe202115.314.5100.099.417.4580.0zimbabwe
6149Zimbabwe202217.516.0100.099.417.4580.0zimbabwe